SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2981–2990 of 3874 papers

TitleStatusHype
Video Super-Resolution Using a Grouped Residual in Residual Network—0
Video Super-Resolution via Deep Draft-Ensemble Learning—0
Video Super-Resolution with Long-Term Self-Exemplars—0
VIINTER: View Interpolation with Implicit Neural Representations of Images—0
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning—0
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval—0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution—0
ViTO: Vision Transformer-Operator—0
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data—0
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified